brain mapping of developmental coordination disorder...this chapter discusses the brain mapping of...

Chapter 3

Brain Mapping ofDevelopmental Coordination Disorder

Mitsuru Kashiwagi and Hiroshi Tamai

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56496

1. Introduction

This chapter discusses the brain mapping of developmental coordination disorder (DCD).DCD is a neurological disorder characterised by impaired motor coordination and impairedperformance of daily activities that require motor skills. In the Diagnostic and StatisticalManual of Mental Disorders, fourth edition (DSM-IV) [1], DCD is included in the LearningDisorders and the Motor Skills Disorders sections [1]. DCD is one of the most common disordersin childhood, and it affects 5% to 6% of school-age children.

DCD is a heterogeneous disorder, and its manifestations are varied and often complex. Ameta-analysis of DCD literature that was published between 1974 and 1996 showed thatthe greatest deficiency in these patients was in visual-spatial processing [2]. The latest meta-analysis of 128 studies suggested that children with DCD show underlying problems in thevisual-motor translation (namely inverse modelling) of movements that are directed withinand outside peripersonal space, adaptive postural control, and the use of predictive control(namely forward modelling), which impacts their ability to adjust movement to changingconstraints in real time [3]. The underlying cognitive mechanisms are still a matter ofdiscussion.

Previous clinical and experimental studies have indicated that motor skill difficulties in DCDchildren may be related to dysfunction in the parietal lobe [4], the cerebellum (CB) [5], the basalganglia (BG) [6], the hippocampus [7] and the corpus callosum [8]. However, because the motorsystem is highly complex, this is not a given conclusion.

Neuroimaging, including functional magnetic resonance imaging (fMRI), will create a newstandard in the understanding of the complex cognitive functions in a child’s brain. Therefore,it is useful to review the data from current DCD neuroimaging studies as the next critical step

© 2013 Kashiwagi and Tamai; licensee InTech. This is an open access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permitsunrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

in enhancing our understanding of DCD. Clarifying DCD pathogenesis will be beneficial toclinicians as well as to children suffering from DCD.

2. Neuroimaging studies of DCD

We researched the Medline database with the terms ‘neuroimaging’ and ‘DCD’ for originalresearch articles that were written in English. There were few DCD neuroimaging studies, andonly 6 neuroimaging studies that involved the direct identification of the neural substratesresponsible for DCD were available (Table 1).

No. Citationtypes of

neuroimagingstudy

numbers ( age ) object task results remarks

1Querne

et al fMRIDCD, 9 :7 boys 2 girls (9.9±1.8 years)control, 10 :7 boys 3 girls (10.0±1.1years)

To assess the impact of DCD oneffective connectivity applied to aputative model of inhibition.

go-nogo[path coefficents]DCD<control, right hemisphere :striatum/parietal cortexDCD>control, left hemisphere :MFC/ACC/IPC

not motor tasksmall sample size

2 Kashiwagiet al

fMRI DCD, 12 :12 boys (129.4±11.6 months)control, 12 :12 boys (125.3±11.9 months)

To detect the mechanisms underlyingclumsiness in DCD children.

visuallyguided

tracking

[brain activity] tracking condition - watching conditionDCD<control, left hemisphere :PPC(SPL,IPL)/postcentral gyrus[magnetic signal change for the DCD and control in IPL]negatively correlated with the task performace

The only study toreveal　significant

correlation of brainactication with task

performance

3Zwicker

et al fMRIDCD, 7 :6 boys 1 girl (10.8±1.5 years)control, 7 :3 boys 4 girls (10.9±1.5years)

To determine whether patterns ofbrain activity differed betweenchildren with and without DCD.

trail-tracing

[brain activity]DCD<control, left hemisphere :percuneus/superior frontal, inferiorp frontal, postcentral (gyrus)p right hemisphere :superior temporal gyrus/insulaDCD>control, left hemisphere : IPLp right hemisphere :middle frontal, supramarginal, lingual,p parahippocampal posterior cingulatep precentral, medial frontal, superiorp temporal (gyrus)/cerebellar lobule VI

small sample size

4Zwicker

et al fMRIDCD, 7 :6 boys 1 girl (10.8±1.5 years)control, 7 :3 boys 4 girls (10.9±1.5years)

To known whether DCD childrenemploy a different set of brainregions than control children duringskilled motor practice.

motor skillpractice oftrail-tracing

[brain activity] retention practice - early praciceDCD<control, left hemisphere :fusiform gyrus/cerebellar lobule VI pp IX /inferior parietal lobulep right hemisphere :inferior parietal lobule/ lingual, middlep frontal (gyrus)/cerebellar lobule I

motor learningparadigm

small sample size

5Maienet al

99m-ECDSPECT DCD, 1 woman (19 years old)

To investigate the neural correlatesof DCD. -

[decrease of perfusion]p left hemisphere :medial prefrontalp right hemisphere :cerebellar/occipital region

Case report19 years old

6 Zwickeret al

DiffusionTensor

Imaging

DCD, 7 :6 boys 1 girl (10.8±1.5 years)control, 7 :3 boys 4 girls (10.9±1.5years)

To explored the integrity of motor,sensory, and cerebellar pathways inchildren with andwithout DCD.

-

Fractional anisotropy of motor and sensory tracts and diffusionparameters in cerebellar peduncles did not differ.Mean diffusivity of the corticospinal tract and posterior thalamicradiation was lower in DCD children.Axial diffusivity was significantly correlated with motor impairmentscores for both the corticospinal tract and posterior thalamic radiation.

not motor tasksmall sample size

Table 1. DCD neuroimaging studies

Table 1. DCD neuroimaging studies

2.1. Four fMRI studies

[No. 1] In 2008, Querne et al. [9] reported that DCD children exhibited abnormal brainhemispheric specialisation during development when performing a go/no-go task. Connec‐tivity analyses in the middle frontal cortex-anterior cingulate cortex-inferior parietal cortex(IPC) network indicated that children with DCD are less able than healthy children to easilyor promptly switch between go and no-go motor responses. This was the first fMRI study toclarify the attentional brain network of DCD children.

[No. 2] In 2009, Kashiwagi et al. [10] (our group) showed poor performance and less activationin the left superior parietal lobe (SPL), the left inferior parietal lobe (IPL), and the left post‐

Functional Brain Mapping and the Endeavor to Understand the Working Brain38

central gyrus in DCD children during visuomotor tasks. This was the first fMRI study toelucidate the neural underpinnings of DCD children by using a visuomotor task. Furthermore,a connection between the brain activity in the left IPL and task performance that representedclumsiness was suggested.

[No. 3] In 2010, Zwicker et al. [11] demonstrated that DCD children activate different brainregions compared to control children when performing the same trail-tracing task. They foundthat a correlation of the activation of the right middle frontal gyrus with the number of tracesindicated cognitive effort in the children with DCD.

[No. 4] In 2011, Zwicker et al. [12] found that DCD children demonstrated decreased activationin cerebellar-parietal and cerebellar-prefrontal networks as well as in brain regions associatedwith visuospatial learning. This was the first study in DCD children to examine changes in thepatterns of brain activation that were associated with skilled motor practice.

2.2. One single-photon emission computed tomography study

[No. 5] In 2010, Marien et al. [13] reported that the CB is crucially implicated in the pathophy‐siological mechanisms of DCD, and this reflects a disruption of the cerebello-cerebral networkthat is involved in executing planned actions, visuospatial cognition, and affective regulation.This was the first single-photon emission computed tomography study of children with DCD.

2.3. One diffusion tensor imaging (DTI) study

[No. 6] In 2012, Zwicker et al. [14] showed that the mean diffusivity of motor and sensorypathways is lower in DCD children. In addition, differences in the intrinsic characteristics ofaxons or in the extra-axonal/extracellular space may underlie some of the deficits that areobserved in DCD children. This was the first DTI study in children with DCD.

3. Different patterns of activation of cerebral areas in DCD patientscompared to controls in fMRI motor control tasks

In order to elucidate the main mechanisms underlying the impaired motor skills in DCDpatients, we have to examine brain activities that are related to motor performances duringmotor control tasks. There were 3 fMRI studies (No. 2, 3, and 4) on motor control tasks in DCDpatients. One study included a motor learning task. The cerebral areas listed below showedsignificant differences in activation between DCD children and control children during themotor control task and motor learning task and the functions of those areas.

Brain Mapping of Developmental Coordination Disorderhttp://dx.doi.org/10.5772/56496

39

3.1. No. 2: Our study

3.1.1. Study design and conditions

The experiment was designed in a block manner and consisted of the following 3 conditions:

1) Tracking condition (TC): tracking the moving blue target by manipulating the joystick,

2) Watching condition (WC): watching the moving red target and white cursor without handmanipulation

and

3) Resting condition (RC): looking at a fixation cross.

Each condition lasted for 24 s and was repeated 6 times in a pseudo-randomised order (Figure1). All of the participants were trained through 40 trials of tracking before scanning. Theparticipants achieved their best performance after several trials. Task performance wasrepresented by the distance (pixels) between the centre of the target and the cursor. Werecorded 6 sets of data on the distance and the velocity changes for each participant, and theeffects of the group and the participants (within group) on these data were analysed by a two-factor nested design analysis of variance. Furthermore, the effects of the trial numbers and theparticipants on the task performance during the final 6 training trials and 6 scanning trialswere analysed with a factorial two-way analysis of variance.

Fixation cross

TargetCursor

Fixation cross

Rest(24 s)

Tracking(24 s)

Cursor and target

(24 s)

Watching(24 s)

Rest(24 s)

Figure 1. Our study (No. 2) design and conditions. The experiment was designed in a block manner and consisted of 3conditions. Each condition lasted 24 s and was repeated 6 times in a pseudo-randomised order.


3.1.2. Behavioural data

Figure 2. (a) shows the behavioural results for a DCD child and a control child. The DCD childshowed much error at the return point and particularly at the beginning point compared tothe control child.

The distance between the target and the cursor and the change in the velocity of the cursorwere significantly greater in the DCD group than in the control group (mean distance, 22.8 vs.19.5 pixels, P = 0.001; mean velocity change, 398.5 vs. 369.9 pixels/s/s, P = 0.013). The numberof trials did not significantly affect task performance in either group over the final 6 trainingtrials and 6 scanning trials [training trials: DCD group, F(5,55) = 0.41, P = 0.839 and F(5,55) =1.20, P = 0.322; control group, F(5,55) = 0.49, P = 0.784] [scanning trials: DCD group, F(5,55) =0.41, P = 0.839; control group, F(5,55) = 0.49, P = 0.780] (Figure 2. (b)).

performance was represented by the distance (pixels) between the centre of the target and the cursor. We recorded 6 sets of data

on the distance and the velocity changes for each participant, and the effects of the group and the participants (within group) on

these data were analysed by a two-factor nested design analysis of variance. Furthermore, the effects of the trial numbers and the

participants on the task performance during the final 6 training trials and 6 scanning trials were analysed with a factorial two-way

analysis of variance.

3.1.2 Behavioural data

Figure 2a shows the behavioural results for a DCD child and a control child. The DCD child showed much error at the return

point and particularly at the beginning point compared to the control child.

The distance between the target and the cursor and the change in the velocity of the cursor were significantly greater in the DCD

group than in the control group (mean distance, 22.8 vs. 19.5 pixels, P = 0.001; mean velocity change, 398.5 vs. 369.9 pixels/s/s,

P = 0.013). The number of trials did not significantly affect task performance in either group over the final 6 training trials and 6

scanning trials [training trials: DCD group, F(5,55) = 0.41, P = 0.839 and F(5,55) = 1.20, P = 0.322; control group, F(5,55) =

0.49, P = 0.784] [scanning trials: DCD group, F(5,55) = 0.41, P = 0.839; control group, F(5,55) = 0.49, P = 0.780] (Fig. 2b).

a b

Figure 2a. The behavioural results of a child with DCD and a control child. The blue line shows the trajectory of the target, the

green line shows the trajectory of the cursor and the red line shows the distance between the target and the cursor.

Figure 2b. Mean task performances for the DCD and control groups during 6 scanning trials. The vertical bars indicate the

standard errors of the means for each data point.

3.1.3 Imaging data

In the comparison of the watching condition versus the resting condition (WC - RC), both the DCD-greater-than-control and

control-greater-than-DCD comparisons did not reveal significant differences in the activation maps between the groups. In the

comparison of the tracking condition versus the watching condition [(TC - RC) - (WC - RC)], greater activation was not

observed in the DCD-greater-than-control comparison. Inversely, the control-greater-than-DCD comparison showed differences

in the activation in the left hemisphere.

Different brain activation in the comparison of the tracking condition versus the watching condition [(TC - RC) - (WC - RC)] in

the visually guided tracking task between DCD patients and controls

(DCD < control only)

Left posterior parietal cortex (SPL and IPL): The main brain region involved in skilled motor functions, eye movements,

0

10

20

30

1 2 3 4 5 6

● DCD group

○ Control group

Th

e d

ista

nce

(Pixel)

Trials

Fig 1

Fig. 2a Fig. 2b

DCD child Control child

Beginning Beginning End End

Fig. 2a Fig. 2b

Figure 2. (a) The behavioural results of a child with DCD and a control child. The blue line shows the trajectory of thetarget, the green line shows the trajectory of the cursor and the red line shows the distance between the target andthe cursor. (b) Mean task performances for the DCD and control groups during 6 scanning trials. The vertical bars indi‐cate the standard errors of the means for each data point.

3.1.3. Imaging data

In the comparison of the watching condition versus the resting condition (WC - RC), both theDCD-greater-than-control and control-greater-than-DCD comparisons did not reveal signifi‐cant differences in the activation maps between the groups. In the comparison of the trackingcondition versus the watching condition [(TC - RC) - (WC - RC)], greater activation was notobserved in the DCD-greater-than-control comparison. Inversely, the control-greater-than-DCD comparison showed differences in the activation in the left hemisphere.

Different brain activation in the comparison of the tracking condition versus the watchingcondition [(TC - RC) - (WC - RC)] in the visually guided tracking task between DCD patientsand controls



41

Left posterior parietal cortex (SPL and IPL): The main brain region involved in skilled motorfunctions, eye movements, multimodal encoding of locations near the head, reaching andpointing movements with the arm and finger, grasping movements that require preshapingof the hand [15], tool use and motor attention [16], internal representation of the dynamic bodyschema [17], hand movements [18] and motor imagery [19]. Left postcentral gyrus: proprio‐ceptive control of movement [20]. (Table 2, Figure 3.(a).)

The correlations between task performance and the maximal magnetic resonance signalchanges within a diameter of 8 mm of each local maximum were analysed. Only the magneticresonance signal changes in the left IPL negatively correlated with task performance [r, -0.413;P < 0.05] (Figure 3(b)).

RegionCluster MNI coordinates (mm)

L/R Size P corrected Z x y z

superior parietal lobe (BA7) L 1024 0.001 4.02 -40 -48 66

postcentral gyrus (BA2) L 3.89 -68 -20 34

inferior parietal lobe (BA40) L 3.83 -36 -52 50

BA, Brodmann area; L, left; R, right

Table 2. Cluster size, Z-values, and coordinates

3.2. No. 3: Zwicker et al. study

Different brain activation in the trail-tracing task between DCD patients and controls

multimodal encoding of locations near the head, reaching and pointing movements with the arm and finger, grasping movements

that require preshaping of the hand [15], tool use and motor attention [16], internal representation of the dynamic body schema

[17], hand movements [18] and motor imagery [19]. Left postcentral gyrus: proprioceptive control of movement [20]. (Table 2,

Figure 3a.)

The correlations between task performance and the maximal magnetic resonance signal changes within a diameter of 8 mm of

each local maximum were analysed. Only the magnetic resonance signal changes in the left IPL negatively correlated with task

performance [r, -0.413; P < 0.05; Figure 3(b)].

Region Cluster 1. 2. MNI coordinates (mm)

3. L/R 4. Size 5. P corrected 6. Z 7. 8. x 9. y 10. z

11. superior parietal lobe (BA7) 12. L 13. 1024 14. 0.001 15. 4.02 16. 17. -40 18. -48 19. 66

20. postcentral gyrus (BA2) 21. L 22. 23. 24. 3.89 25. 26. -68 27. -20 28. 34

29. inferior parietal lobe (BA40) 30. L 31. 32. 33. 3.83 34. 35. -36 36. -52 37. 50

BA, Brodmann area; L, left; R, right

Table 2. Cluster size, Z-values, and coordinates

Figure 3(a). Figure 3(b).

Figure 3(a). Brain activity differences between the DCD and control groups. In the comparison of the tracking condition versus

the watching condition, the control-greater-than-DCD comparison showed differences in left hemisphere activation in the left

SPL and IPL and the left postcentral gyrus (P < 0.001 at the voxel level and P < 0.05 with a correction for multiple comparisons

at the cluster level).

Figure 3(b). Mean task performances and magnetic resonance signal changes for the DCD and control groups in the IPC. The

vertical bars indicate the standard errors of the means, and the horizontal bars indicate the 90% confidence intervals for each data

point.

3.2 No. 3: Zwicker et al. study

Different brain activation in the trail-tracing task between DCD patients and controls

(DCD < control)

Left precuneus: visuospatial processing and initiation of movement programming [21,22]. Left superior frontal gyrus: spatially

oriented processing [23]. Left inferior frontal gyrus: inhibitory control over motor responses [24]. Left postcentral gyrus: motor

0

20

40

60

80

-0.6 -0.2 0.2 0.6 1 1.4

(PIXEL)

MR signal change (%)

The

dis

tan

ce

DCD child

Control child

(b)

Figure 3. (a) Brain activity differences between the DCD and control groups. In the comparison of the tracking condi‐tion versus the watching condition, the control-greater-than-DCD comparison showed differences in left hemisphereactivation in the left SPL and IPL and the left postcentral gyrus (P < 0.001 at the voxel level and P < 0.05 with a correc‐tion for multiple comparisons at the cluster level); (b). Mean task performances and magnetic resonance signalchanges for the DCD and control groups in the IPC. The vertical bars indicate the standard errors of the means, andthe horizontal bars indicate the 90% confidence intervals for each data point.


(DCD < control)

Left precuneus: visuospatial processing and initiation of movement programming [21, 22].Left superior frontal gyrus: spatially oriented processing [23]. Left inferior frontal gyrus:inhibitory control over motor responses [24]. Left postcentral gyrus: motor control and motorlearning [25, 26]. Right insula: motor control [27], motor learning [28] and error processing [29].

(DCD > control)

Left IPL: interpretation of sensory information [30, 31]. Right supramarginal gyrus: visuo‐motor/visuospatial processing [32, 33]. Right posterior cingulate gyrus: spatial attention [34].Right lingual gyrus: visuospatial processing [35]. Right precentral and parahippocampalgyri: spatial memory [36-38]. Right CB (lobule VI): spatial processing [39].

3.3. No. 4: Zwicker et al. study

Different brain activation of the retention condition versus the early condition in the trail-tracing task between DCD patients and controls


Right cerebellar crus I: working memory and executive functions [40]. Left cerebellar lobuleVI: part of the sensorimotor network of the CB [40], spatial processing [41], performance of avariety of tasks, including serial reaction time tasks [42], motor sequence learning [43], reachingtasks [44] and planned, discretely aimed arm movements [45] as well as the magnitude ofmotor correction during visuomotor learning [46]. Left cerebellar lobule IX: unclear [40].Right IPL: spatial working memory [47]. IPL: the processing of sensory information and visualfeedback [48]. Right dorsolateral prefrontal cortex: the initial stages of explicit motor learning[49], motor and visuomotor sequences [50, 51] and attentional control [52]. Left fusiformgyrus: higher level visual and visuospatial processing during the consolidation of visuomotorlearning [53]. Right lingual gyrus: visuospatial processing [54].

4. Discussion

4.1. Previous studies of DCD (terminology, clumsiness, motor learning, and brain area)

4.1.1. What is DCD? Historical perspectives

At the beginning of the 20th century, an awareness of different levels of motor performancewas clearly described in studies that identified the motor abilities of children as very clever,clever, medium, awkward or very awkward [55]. As early as 1926, Lippitt was concernedspecifically with poor muscular coordination in children [56]. Orton’s (1937) discussion ofdevelopmental apraxia or abnormal clumsiness was strongly influenced by ideas about adultapraxia and damage to the dominant hemisphere [57]. Since the early 1960s, many terms havebeen used to describe children whose motor difficulties interfere with daily living, and theseinclude developmental apraxia and agnosia, minimal cerebral dysfunction (Wigglesworth,


43

1963) [58], minimal brain dysfunction (Clements, 1966) [59], minimal cerebral palsy (Kong,1963) [60] and developmental dyspraxia [61].

At a 1994 consensus meeting in London, Ontario (Polatajko et al., 1995) [62], a multidisci‐plinary group of internationally recognised researchers who work with children with motorclumsiness agreed to use the term developmental coordination disorder as described by theAmerican Psychiatric Association (APA) in the DSM-IIIR (APA, 1987) and revised in DSM-IV (APA, 1994).

4.1.2. What is clumsiness? What is dexterity?

Clumsiness is defined by Morris and Whiting as a maladaptive motor behaviour in relation toexpected or required movement performance [63]. The antonym of clumsiness is dexterity.

Dexterity is the ability to find a motor solution for any external situation or to adequately solveany emerging motor problem correctly (adequately and accurately), quickly (with respect toboth decision making and achieving a correct result), rationally (expediently and economical‐ly) and resourcefully (quick-wittedly and initiatively). In many movements and actions, thereare no absolutely unpredictable events, but these movements nevertheless require quick andaccurate movement adaptation to external events that cannot be predicted with certainty. Thisaccurate movement adaptation is important for dexterity. The heart of the problem is to quicklyand correctly find a solution in conditions of an unexpectedly changed environment. Dexterityapparently is not in the motor action itself but is revealed by its interaction with changingexternal conditions, including the uncontrolled and unpredicted influences from the environ‐ment. The established essential feature of dexterity is that it always refers to the external world.Moreover, dexterity is a complex activity. Real-life movements have an element of adaptionto various, although perhaps minor, unexpected events [64].

Quick and correct motion is fundamental to dexterity performance. Quick motion means therapid initiation of action and fleetness of the performance itself. Accurate motion impliesspatially and temporally accurate performance. As we move more rapidly, we become moreinaccurate in terms of the goal we are trying to achieve. The adage haste makes waste has beena long-standing viewpoint about motor skills.

Identifying optimal measurements of skill learning is not trivial [65]. Previous studies havetypically defined skill acquisition in terms of a reduction in the speed of movement executionor reaction times, increases in accuracy or decreases in movement variability. Yet, thesemeasurements are often interdependent, in that, faster movements can be performed at thecost of reduced accuracy and vice versa, which is a phenomenon which has often been referredto as the speed-accuracy trade-off. The principles of speed-accuracy trade-offs, which areknown as Fitts’ law, are specific to the goal and nature of the movement tasks [66]. One solutionto this issue is through the assessment of changes in the speed-accuracy trade-off functions.Therefore, we should assess task performances with both speed and accuracy. The visuallyguided tracking task that we adopted in our fMRI study has been experimentally used forevaluating motor skills. We assessed task performance as the change in the velocity of thecursor for speed and the distance between the target and the cursor for accuracy.


4.1.3. What is motor learning?

Children with DCD have difficulties with motor performance and motor learning [67]. Mostclinicians and researchers agree that difficulty with motor learning is a key feature of DCD.

Motor learning depends on maturation, experience, and active learning. Motor learning hasbeen described as a set of processes that are associated with practice or experience and thatlead to relatively permanent changes in the capabilities for producing skilled actions [68].Motor skill learning means, in other words, dexterity learning. Therefore, as mentioned above,accurate movement adaptation is an important fact in motor skill learning.

For motor learning, 3 main theories apply. Fitts and Posner (1967) distinguished the following3 phases of motor learning: cognitive, associative and autonomous [69].

Hikosaka and colleagues proposed a model of motor skill learning. According to this model,2 parallel loop circuits operate in the learning of the spatial and motor features of sequen‐ces. Whereas the learning of spatial coordinates is supported by the frontoparietal associa‐tive BG-CB circuit, the learning of motor coordinates is supported by the primary motorcortex-sensorimotor BG-CB circuit. According to this model, transformations between the2 coordinate systems rely on the contribution of the supplementary motor area (SMA), thepre-supplementary motor area (preSMA) and the pre-motor cortices. Importantly, it hasbeen suggested that the learning of spatial coordinates is faster, yet requires additionalattentional and executive resources that are putatively provided by prefrontal corticalregions [70] (Figure 4. (a)).

Similarly, on the basis of brain imaging studies, Doyon and Ungerleider (Doyon and Unger‐leider’s model of motor skill learning) [71] proposed that cerebral plasticity is important withinthe cortico-striatal and cortico-cerebellar systems during the course of learning a new sequenceof movements (motor sequence learning) or the adaptation to environmental perturbations(motor adaptation).

This model proposes that, depending upon the nature of the cognitive processes that arerequired during learning, both motor sequence and motor adaptation tasks recruit thefollowing similar cerebral structures early in the learning phase: the striatum, CB, motorcortical regions, in addition to prefrontal, parietal, and limbic areas. Dynamic interactionsbetween these structures are likely to be crucial in establishing the motor routines that arenecessary for the learning of the skilled motor behaviour. A shift of the motor representationfrom the associative to the sensorimotor striatal territory can be seen during sequence learning,whereas additional representation of the skill can be observed in the cerebellar nuclei afterpractice in a motor adaptation task. When consolidation has occurred, the subject has achievedasymptotic performance, and their performance has become automatic; however, the neuralrepresentation of a new motor skill at that stage is believed to be distributed in a network ofstructures that involves the cortico-striatal or cortico-cerebellar circuit, depending on the typeof motor learning acquired. At this stage, the model suggests that the striatum is no longernecessary for the retention and execution of the acquired skill for motor adaptation; regionsrepresenting the skill at this stage include the CB and related cortical regions. In contrast, areverse pattern of plasticity is thought to occur in motor sequence learning, such that the CB


45

is no longer essential with extended practice, and the long-lasting retention of the skill isbelieved at this stage to involve representational changes in the striatum and the associatedmotor cortical regions (Figure 4. (b)).

(a) (b)

Figure 4. (a). Hikosaka et al.’s scheme of motor skill learning. The figure is from Curr Opin Neurobiol 2002;12(2)217-222.; (b). Doyon et al.’s model of skill learning. The figure is from Curr Opin Neurobiol 2005;15(2) 161-167.

Both models share the view that motor skill learning involves interactions between distinctcortical and subcortical circuits that are crucial for the unique cognitive and control demandsthat are associated with this stage of skill acquisition [65].

4.1.4. Where is brain area associated with DCD?

The parietal lobe

The parietal lobe plays a critical role in numerous cognitive functions, particularly in thesensory control of action [72]. As we know, lesions in the left posterior parietal cortex (PPC)are associated with apraxia, which is a higher order motor disorder, whereas lesions in theright PPC are associated with unilateral neglect, which is an attentional disorder [73].

The results of a meta-analysis of the information processing deficits that are associated withDCD children showed that DCD children have significantly poorer visual spatial processingthan healthy controls [2, 3]. This evidence suggests that the parietal lobe may be implicated inDCD children because of its primary role in the processing of visual spatial information [74].In addition, DCD children are less competent in their ability to recognise emotion [75], whichhas been linked to parietal lobe involvement [76]. Some clinical studies have supported the


notion that the parietal lobe is associated with the mechanisms underlying the impaired motorskills in DCD children. Wilson et al. [77] conducted a study on procedural learning in DCDchildren and stated that the neurocognitive underpinnings of the disorder may be located inthe parietal lobe and not in the BG. Another study involving mental rotation tasks indicatedthat DCD children might have dysfunction in the parietal lobe, which is involved in the internalrepresentation of the movement [78]. In a recent study, Hyde et al. found that children withDCD show a similar response pattern as patients with lesions of the PPC on a number ofparadigms that assess aspects of internal modelling. This has led to the hypothesis that DCDmay be attributable to dysfunction at the level of the PPC [79].

Furthermore, a study on imagined motor sequences revealed that the performance of real andimagined tasks are dissociated in DCD children; this finding indicates that a disruption in themotor networks of the parietal lobe is associated with the generation of the internal represen‐tations of motor acts [80]. In addition, this group found that the ability of motor imagery inDCD children varied according to their level of motor impairment [81], and motor imagerytraining ameliorated the clumsiness in DCD children [82]. Recommendations of the definition,diagnosis, and intervention of DCD by the European Academy for Childhood Disability [3]only refer to the fact that Katschmarsky considered parietal dysfunction an underlying organicdefect in DCD children from their study [4].

The cerebellum

The CB is related to motor skill learning. Given the CB’s role in motor coordination and posturalcontrol, it may be involved in the neuropathology of DCD [74]. Geuze reported that the majorcharacteristics of poor control in DCD are the inconsistent timing of muscle activationsequences, co-contraction, a lack of automation and the slowness of response. Convergingevidence indicates that cerebellar dysfunction contributes to the motor problems of childrenwith DCD [83]. Motor adaptation, which is also thought to reflect cerebellar function [71], hasbeen demonstrated in children with DCD [84]. Waelvelde reported that the parameterizationof movement execution in the Rhythmic Movement Test in children with DCD was signifi‐cantly less accurate both in time and in space than the performance of same-aged typicallydeveloping children. The data of that study support the notion that some children with DCDmanifest impairments in the generation of internal representations of motor actions andsupport the hypothesis that there is some form of cerebellar dysfunction in some children withDCD [85].

The basal ganglia

The BG is involved in motor control and motor skill learning. Clumsiness is a term that isassociated in childhood with problems in the learning and execution of skilful movements, theneuronal basis of which is, however, poorly understood. Groenewegen reported that, as far asdeficient motor programming is involved, the BG probably plays a role [86]. Wilson et al. didnot identify any evidence that the BG is implicated in DCD [77].

The hippocampus

Hippocampal, cortico-cerebellar, and cortico-striatal structures are crucial for building themotor memory trace [71]. Neural structures, such as the hippocampus, parietal cortex, and CB,


47

have been proposed to contribute to the process of learning new motor sequences. Gheysen etal. found that the sequence learning problems of DCD children might be located at the stageof motor planning rather than at sequence acquisition [87]. The fact that the hippocampus andCB could be involved in the neuropathology of DCD has been frequently proposed given theirfunction in motor coordination and adaptation [71,88].

The corpus callosum

Sigmundsson reported that only DCD children showed significant performance differences infavour of the preferred hand in visual/proprioceptive or proprioceptive conditions. Thisfinding was thought to suggest that the developmental lag that is exhibited by DCD childrenmight have pathological overtones that are possibly related to the development of the corpuscallosum [89].

4.2. Present studies of DCD (neuroimaging studies and current conclusion)

4.2.1. Recent neuroimaging studies

The parietal lobe and CB are key brain regions that have been highlighted in recent neuroi‐maging studies of the visuomotor performance of children with DCD. The brain functions ofthese 2 regions are known to involve the motor adaptation of motor learning in the past 2models of motor skill learning. In addition, the parietal lobe and striatum are known to beinvolved in the motor sequence learning of motor learning. Accordingly, the parietal lobe is aregion that is associated with sensory input, motor output, motor adaptation, and motorsequence learning.

In our results, parietal dysfunction reflected the difference in brain activities between DCDand control children during the phase of automation. The task in our study was easy to master,and, therefore, the performances of DCD patients and controls had already reached theirplateau before the scanning trials. Thus, this study did not involve motor learning effects. Inour fMRI task, the speed of the target was changed sinusoidally during its 12-s round trip.Consequently, we studied both motor sequence learning and motor adaptation in our fMRItask. We reported that DCD children showed poor performance and less activation in the leftPPC and postcentral gyrus during the visuomotor task. Thus, a connection was suggestedbetween brain activity of the left PPC and clumsiness.

In the results from other studies, dysfunction of the CB may reflect the different brain activitiesof consolidation conditions versus cognitive processes between DCD children and controlsduring the early–slow learning phases. In that study, tracing accuracy in control childrenimproves from early practice to consolidation and shows increased activation in several brainregions. In contrast, the DCD children did not show any improvements in tracing accuracy.The authors noted that further work with a larger sample is needed to confirm the hypothesisthat these areas of brain activation may contribute to improved motor performance. In thisstudy design, the results mainly showed motor adaptation.


4.2.2. Current conclusion: Why are DCD children clumsy?

DCD is a disorder of impaired performances in daily activities that require motor skill. Themovement parameters of daily activities appear to be encoded by delayed recall and requireeasy motor skills. Even though DCD children can learn easy motor skills, why they usuallyrequire more practice than healthy children and their quality of movement may be compro‐mised is a pressing question.

From the viewpoints of the recent model of motor skill learning, previous studies, and therecent neuroimaging studies, DCD children have some difficulties with the cognitive processesin the fast learning phase, consolidation in the slow learning phase and automation in theretention phase during simple and easy motor skill learning (motor sequence learning andmotor adaptation). Accordingly, it is not always easy for DCD children to perfectly acquireeven simple motor skills. In fact, real-life movements that are required for daily simple andeasy activities require an element of adaption to various, although perhaps minor, unexpectedevents. Therefore, we assumed that DCD children always seem to be required to adapt to thedaily simple and easy activities as unexpected new motor learning every day because it is hardfor DCD children to perfectly consolidate motor skills. In addition, DCD children show thatthe more a task demands the integration and adaptation of different information, the morevulnerable it is. Accordingly, we considered that motor adaptation is more important thanmotor sequence learning for DCD children.

Given that the main clinical finding in DCD children is motor adaptation, dysfunction in theparietal lobe and CB contributes to the mechanism underlying DCD. In addition, consideringthat DCD children have problems with sensory input and motor output, we conclude that theparietal lobe is the main neural substrate that is responsible for DCD.

4.3. Future studies of DCD (mirror neuron system, functional connectivity approaches,default mode network, intervention, and motor imagery training)

4.3.1. The mirror neuron system hypothesis

DCD includes impairments in motor skills, motor learning, and imitation. A better under‐standing of the neural correlates of the motor and imitation impairments in DCD childrenholds the potential for informing the development of treatment approaches that can addressthese impairments. In recent years, the discovery of a frontoparietal circuit, which is knownas the mirror neuron system (MNS), has enabled researchers to better understand imitation,general motor functions, and aspects of social cognition. Given its involvement in imitationand other motor functions, they propose that dysfunction in the MNS may underlie thecharacteristic impairments of DCD [90].

4.3.2. Functional connectivity approaches

Most past studies of brain function have built on the concept of the localisation of function, inthat different brain regions support different forms of information processing. Yet, no brainregion exists in isolation. Information flows between the regions through the action potentials


49

that are conducted by axons, which are bundled into large fibre tracts. For more than a century,neuroanatomists have mapped the anatomical connections between brain regions in anattempt to understand the structural connectivity of the brain. While much remains to bediscovered, the study of the anatomical connections between brain regions has provided acornerstone for neuroscience research.

Despite the value of this anatomical research, the knowledge of the structural connectionsbetween brain regions can only provide a limited picture of information flow in the brain.Descriptions of functional connectivity or of how the activity of one brain region influencesactivity in another brain region are also needed. Many researchers who are interested infunctional connectivity have adopted fMRI techniques because of their utility for measuringchanges in activation throughout the entire brain. This approach is useful for the brainmapping of DCD [91]

4.3.3. Default mode network

Functional brain imaging studies with fMRI in normal human subjects have consistentlyrevealed expected task-induced increases in regional brain activity during goal-directedbehaviours. These changes are detected when comparisons are made between a task state,which is designed to place demands on the brain, and a resting state with a set of demandsthat are uniquely different from those of the task state. Functional imaging studies shouldconsider the need to obtain information about the baseline.

Researchers have also frequently encountered task-induced decreases in regional brainactivity, even when the control state consists of the subject lying quietly with their eyes closedor passively viewing a stimulus. Whereas cortical increases in activity have been shown to betask specific and therefore to vary in location depending on the task demands, many decreasesappear to be largely task independent and to vary little in their location across a wide rangeof tasks. This consistency with which certain areas of the brain participate in these decreasesmakes us wonder whether there might be an organised mode of brain function that is presentas a baseline or default state [92]. Spatial patterns of spontaneous fluctuations in bloodoxygenation level-dependent signals reflect the underlying neural architecture. The study ofthe brain networks that are based on these self-organised patterns is termed resting-state fMRI.

The notion of a default mode of brain function (DMN) has taken on certain relevance in humanneuroimaging studies and in relation to a network of lateral parietal and midline corticalregions that show prominent activity fluctuations during the resting state [93]. The DMN is aprominent large-scale brain network that includes the ventral medial prefrontal cortex, theposterior cingulate/retrospenial cortex, the IPL, the lateral temporal cortex, dorsal medialprefrontal cortex, and hippocampal formation [94]. The parietal lobe is also an important areain the DMN. The DMN is unique in terms of its high resting metabolism, deactivation profileduring cognitively demanding tasks and increased activity during the resting state and high-level social cognitive tasks. There is growing scientific interest in understanding the DMNunderlying the resting state and higher-level cognition in humans. A recent study found thata goodness-of-fit analysis applied at the individual subject level suggested that the activity in


the default-mode network might ultimately prove to be a sensitive and specific biomarker forincipient Alzheimer’s disease [95].

The functional and structural maturation of networks that are comprised of discrete regionsis an important aspect of brain development. The putative functions of the DMN, as well asthe maturation of cognitive control mechanisms, develop relatively late in children, and theyare often compromised in neurodevelopmental disorders, such as autism spectrum disordersand attention-deficit/hyperactivity disorder [96]. The relationship between DMN structureand function in DCD children is not known. Examining the developmental trajectory of theDMN is important not only for the understanding of how the structures of the brain changeduring development and impact the development of key functional brain circuits, but also forunderstanding the ontogeny of cognitive processes that are subserved by the DMN [97]. Thesemultimodal imaging analyses will be important for a better understanding of how local andlarge-scale anatomical changes shape and constrain typical and atypical functional develop‐ment. Future research should systematically explore the developmental trajectory of the DMNin a normal population and compare this with the maturation of the DMN in DCD children.

4.3.4. Intervention

Can dexterity be individually developed? Is it an exercisable capacity? The answer is positiveand multifaceted. It is obvious that natural, inborn, and constitutional prerequisites fordexterity are and will be as different in different persons as their other psychophysical abilities.The attainable individual peaks of development, the degrees of difficulty, and the necessaryamount of time for achieving a certain result will inevitably cause great individual variations.It is much more important to state that all natural prerequisites for dexterity can be developed.Both aspects of the structural complex that result in use dexterity can be exercised anddeveloped.

In a systematic review of interventions on DCD children, Hillier generally concluded that anintervention for DCD is better than no intervention [98]. Independently, the guideline groupperformed a systematic literature search of studies that were published from 1995 to 2010.There is sufficient evidence that physiotherapy and/or occupational therapy intervention arebetter than no interventions for DCD children [3].

There are many different treatment approaches for DCD. The approaches to interventionsare divided into the following 2 categories: process-oriented or bottom-up and task-oriented or top-down [99]. Process-oriented approaches include sensory integration therapy,kinaesthetic training, and perceptual motor therapy. Task-oriented approaches includeCognitive-Orientation to Occupational Performance, neuromotor task training, and motorimagery training [3]. In addition, studies have shown that process-oriented approaches maysometimes be effective but are less so than the task-oriented approaches, which are basedon motor learning theories [100].


51

4.3.5. Motor imagery training

Motor imagery (MI) training is a cognitive approach that was developed by Wilson [101]. Ituses the internal modelling of movements that facilitate the child in predicting the consequen‐ces for actions in the absence of overt movement. MI is a new intervention method for DCDchildren. The past literature has already described MI training as a method in stroke rehabil‐itation [102, 103]. MI training was investigated once in a randomised controlled trial, and itshowed a positive effect if it was combined with active training [81].

In an fMRI study that investigated whether the neural substrates mediating MI differed amongparticipants showing high or poor MI ability, intergroup comparisons revealed that goodimagers exhibited more activation in the parietal and ventrolateral premotor regions, whichare known to play a critical role in the generation of mental images [104]. Our data alsoindicated that dysfunction in the parietal lobe, such as that in motor imagery, might be amechanism underlying the motor skill deficits in DCD children. Thus, from our data, MItraining may be a helpful strategy for DCD children.

5. Conclusion

From clinical and neuroimaging studies and models of motor skill learning, we conclude thatparietal lobe dysfunction is the main mechanism underlying DCD. In addition, the parietallobe is a key area of the MNS and MI training. However, the parietal lobe is not the only neuralcorrelate brain region in DCD. Dysfunctions in the CB, striatum, and hippocampus are alsorelated to the neurobiology underlying DCD. In order to further elucidate the pathogenesisand interventions of DCD, additional neuroimaging studies that include DMN and DTI areneeded that link the neural networks and the functional connectivity of brain regions duringmotor performance.

Author details

Mitsuru Kashiwagi1,2 and Hiroshi Tamai3

1 Department of Pediatrics, Hirakata-City Hospital, Osaka, Japan

2 Department of Developmental Brain Science, Osaka Medical College, Osaka, Japan

3 Department of Pediatrics, Osaka Medical College, Osaka, Japan


References

[1] American Psychiatric Association. Diagnostic and statistical manual of mental disor‐ders (4th ed). Washington DC: American Psychiatric Association; 1994.

[2] Wilson PH, McKenzie BE. Information processing deficits associated with develop‐mental coordination disorder: a meta-analysis of research findings. J Child PsycholPsychiatry 1998;39(6) 829-840.

[3] Blank R, Smits-Engelsman B, Polatajko H, Wilson P. European Academy for Child‐hood Disability (EACD): recommendations on the definition, diagnosis and interven‐tion of developmental coordination disorder (long version). Dev Med Child Neurol2012;54(1) 54-93.

[4] Katschmarsky S, Cairney S, Maruff P, Wilson PH, Currie J. The ability to execute sac‐cades on the basis of efference copy: impairments in double-step saccade perform‐ance in children with developmental co-ordination disorder. Exp Brain Res2001;136(1) 73-78.

[5] Van Waelvelde H, De Weerdt W, De Cock P, Janssens L, Feys H, Smits EngelsmanBC. Parameterization of movement execution in children with developmental coordi‐nation disorder. Brain Cogn 2006;60(1) 20–31.

[6] Groenewegen HJ. The basal ganglia and motor control. Neural Plast 2003;10(1-2)107–120.

[7] Gheysen F, Van Waelvelde H, Fias W. Impaired visuo-motor sequence learning inDevelopmental Coordination Disorder. Res Dev Disabil 2011;32(2) 749-756.

[8] Sigmundsson H. Perceptual deficits in clumsy children: inter- and intra-modalmatching approach-a window into clumsy behavior. Neural Plast 2003;10(1-2) 27-38.

[9] Querne L, Berquin P, Vernier-Hauvette MP, Fall S, Deltour L, Meyer ME, de MarcoG. Dysfunction of the attentional brain network in children with developmental co‐ordination disorder: A fMRI study. Brain Res 2008;1244 89–102.

[10] Kashiwagi M, Iwaki S, Narumi Y, Tamai H, Suzuki S. Parietal dysfunction in devel‐opmental coordination disorder: a functional MRI study. Neuroreport 2009;20(15)1319-1324.

[11] Zwicker JG, Missiuna C, Harris SR, Boyd LA. Brain activation of children with devel‐opmental coordination disorder is different than peers. Pediatrics 2010;126(3)e678-686.

[12] Zwicker JG, Missiuna C, Harris SR, Boyd LA. Brain activation associated with motorskill practice in children with developmental coordination disorder: an fMRI study.Int J Dev Neurosci 2011;29(2) 145-152.


53

[13] Mariën P, Wackenier P, De Surgeloose D, De Deyn PP, Verhoeven J. Developmentalcoordination disorder: disruption of the cerebello-cerebral network evidenced bySPECT. Cerebellum 2010;9(3) 405-410.

[14] Zwicker JG, Missiuna C, Harris SR, Boyd LA. Developmental coordination disorder:a pilot diffusion tensor imaging study. Pediatr Neurol 2012;46(3) 162-167.

[15] Culham JC, Cavina-Pratesi C, Singhal A. The role of parietal cortex in visuomotorcontrol: what have we learned from neuroimaging? Neuropsychologia 2006;44(13)2668–2684.

[16] Iacoboni M. Visuo-motor integration and control in the human posterior parietal cor‐tex: evidence from TMS and fMRI. Neuropsychologia 2006;44(13) 2691–2699.

[17] Ogawa K, Inui T. Lateralization of the posterior parietal cortex for internal monitor‐ing of self-generated versus externally generated movements. J Cogn Neurosci2007;19(11) 1827–1835.

[18] Taira M, Kawashima R, Inoue K, Fukuda H. A PET study of axis orientation discrimi‐nation. Neuroreport 1998;9(2) 283–288.

[19] Gerardin E, Sirigu A, Lehéricy S, Poline JB, Gaymard B, Marsault C, Agid Y, Le BihanD. Partially overlapping neural networks for real and imagined hand movements.Cereb Cortex 2000;10(11) 1093–1104.

[20] Grefkes C, Ritzl A, Zilles K, Fink GR. Human medial intraparietal cortex subservesvisuomotor coordinate transformation. Neuroimage 2004;23(4) 1494–1506.

[21] Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and be‐havioural correlates. Brain　2006;129(pt 3) 564 –583.

[22] Treserras S, Boulanouar K, Conchou F, Simonetta-Moreau M, Berry I, Celsis P, Chol‐let F, Loubinoux I. Transition from rest to movement: brain correlates revealed byfunctional connectivity. Neuroimage 2009;48(1) 207–216.

[23] du Boisgueheneuc F, Levy R, Volle E, Seassau M, Duffau H, Kinkingnehun S, Sam‐son Y, Zhang S, Dubois B. Functions of the left superior frontal gyrus in humans: alesion study. Brain 2006;129(pt 12) 3315–3328.

[24] Swick D, Ashley V, Turken AU. Left inferior frontal gyrus is critical for response in‐hibition. BMC Neurosci 2008;9: 102.

[25] Cunnington R, Windischberger C, Deecke L, Moser E. The preparation and executionof self-initiated and externally-triggered movement: a study of event-related fMRI.Neuroimage 2002;15(2) 373―385.

[26] Frutiger SA, Strother SC, Anderson JR, Sidtis JJ, Arnold JB, Rottenberg DA. Multi‐variate predictive relationship between kinematic and functional activation patternsin a PET study of visuomotor learning. Neuroimage 2000;12(5) 515–527.


[27] Fink GR, Frackowiak RS, Pietrzyk U, Passingham RE. Multiple nonprimary motorareas in the human cortex. J Neurophysiol 1997;77(4) 2164 –2174.

[28] Mutschler I, Schulze-Bonhage A, Glauche V, Demandt E, Speck O, Ball T. A rapidsound-action association effect in human insular cortex. PLoS One 2007;2(2) e259.

[29] Ullsperger M, von Cramon DY. Neuroimaging of performance monitoring: error de‐tection and beyond. Cortex 2004;40(4 –5) 593– 604.

[30] Clower DM, West RA, Lynch JC, Strick PL. The inferior parietal lobule is the target ofoutput from the superior colliculus, hippocampus,and cerebellum. J Neurosci2001;21(16) 6283– 6291.

[31] Halsband U, Lange RK. Motor learning in man: a review of functional and clinicalstudies. J Physiol Paris 2006;99(4 – 6) 414–424.

[32] Bjoertomt O, Cowey A, Walsh V. Near space functioning of the human angular andsupramarginal gyri. J Neuropsychol　2009;3(pt1) 31– 43.

[33] Medina J, Kannan V, Pawlak MA, Kleinman JT, Newhart M, Davis C, Heidler-GaryJE, Herskovits EH, Hillis AE. Neural substrates of visuospatial processing in distinctreference frames: evidence from unilateral spatial neglect. J Cogn Neurosci2009;21(11) 2073–2084.

[34] Small DM, Gitelman DR, Gregory MD, Nobre AC, Parrish TB, Mesulam MM. Theposteriorcingulate and medial prefrontal cortex mediate the anticipatory allocation ofspatial attention. Neuroimage 2003;18(3) 633– 641.

[35] Clements-Stephens AM, Rimrodt SL, Cutting LE. Developmental sex differences inbasic visuospatial processing: differences in strategy use? Neurosci Lett 2009;449(3)155–160.

[36] Leung HC, Oh H, Ferri J, Yi Y. Load response functions in the human spatial work‐ing memory circuit during location memory updating. Neuroimage 2007;35(1) 368 –377.

[37] Eichenbaum H, Lipton PA. Towards a functional organization of the medial tempo‐ral lobe memory system: role of the parahippocampal and medial entorhinal corticalareas. Hippocampus 2008;18(12) 1314–1324.

[38] Okudzhava VM, Natishvili TA, Gurashvili TA, Gogeshvili KSh, Chipashvili SA, Bag‐ashvili TI, Andronikashvili GT, Kvernadze GG, Okudzhava NV. Spatial recognitionin cats: effects of parahippocampal lesions. Neurosci Behav Physiol 2009;39(7) 613–618.

[39] Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: ameta-analysis of neuroimaging studies. Neuroimage 2009;44(2) 489 –501.


55

[40] Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, Greicius MD.Distinct cerebellar contributions to intrinsic connectivity networks. Journal of Neuro‐science 2009;29(26) 8586–8594.

[41] Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: ameta-analysis of neuroimaging studies. Neuroimage 2009;44(2) 489–501.

[42] Seidler RD, Purushotham A, Kim SG, Uğurbil K, Willingham D, Ashe J. Cerebellumactivation associated with performance change but not motor learning. Science2002;296(5575) 2043–2046.

[43] Orban P, Peigneux P, Lungu O, Albouy G, Breton E, Laberenne F, Benali H, MaquetP, Doyon J. The multifaceted nature of the relationship between performance andbrain activity in motor sequence learning. Neuroimage 2010;49(1) 694–702.

[44] Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R. Neural correlates of reach er‐rors. Journal of Neuroscience 2005;25(43) 9919–9931.

[45] Boyd LA, Vidoni ED, Siengsukon CF, Wessel BD. Manipulating time-to- plan alterspatterns of brain activation during the Fitts’ task. Experimental Brain Research2009;194(4) 527–539.

[46] Grafton ST, Schmitt P, Van Horn J, Diedrichsen J. Neural substrates of visuomotorlearning based on improved feedback control and prediction. Neuroimage2008;39(3)1383–1395.

[47] Anguera JA, Reuter-Lorenz PA, Willingham DT, Seidler RD. Contributions of spatialworking memory to visuomotor learning. J Cogn Neurosci 2010;22(9) 1917-1930.

[48] Clower DM, West RA, Lynch JC, Strick PL. The inferior parietal lobule is the target ofoutput from the superior colliculus, hippocampus, and cerebellum. J Neurosci2001;21(16) 6283-6291.

[49] Halsband U, Lange RK. Motor learning in man: a review of functional and clinicalstudies.J Physiol Paris. 2006 ;99(4-6) 414-424.

[50] Jenkins IH, Brooks DJ, Nixon PD, Frackowiak RS, Passingham RE. Motor sequencelearning: a study with positron emission tomography. J Neurosci. 1994 Jun;14(6):3775-90.

[51] Sakai K, Hikosaka O, Miyauchi S, Takino R, Sasaki Y, Pütz B. Transition of brain acti‐vation from frontal to parietal areas in visuomotor sequence learning.J Neurosci 1998Mar 1;18(5):1827-40.

[52] Fassbender C, Murphy K, Foxe JJ, Wylie GR, Javitt DC, Robertson IH, Garavan H. Atopography of executive functions and their interactions revealed by functional mag‐netic resonance imaging.Brain Res Cogn Brain Res 2004;20(2) 132-143.


[53] Graydon FX, Friston KJ, Thomas CG, Brooks VB, Menon RS. Learning-related fMRIactivation associated with a rotational visuo-motor transformation. Brain Res CognBrain Res 2005;22(3) 373-383.

[54] Clements-Stephens AM, Rimrodt SL, Cutting LE. Developmental sex differences inbasic visuospatial processing: differences in strategy use?Neurosci Lett 2009;449(3)155-160.

[55] Bagley, W. C. On the correlation of mental and motor ability in school children. TheAmerican Journal of Psychology 1901;12(2) 193-205.

[56] Lippitt, L. C. A manual of corrective gymnastics. New York: Macmillan; 1926

[57] Orton, S.T. Reading, writing and speech problems in children. New York: Norton;1937.

[58] Wigglesworth, R. The importance of recognising minimal cerebral dysfunction inpaediatric practice. In: Bax M, MacKeith R. (ed.) Minimal cerebral dysfunction. LittleClub Clinics in Developmental Medicine No. 10. London: Heinemann Medical; 1963.p.34-38.

[59] Clements, SD. Minimal brain dysfunction in children; Terminology and identifica‐tion. Phase I of a Three-Phase Project, NINBD Monograph 3. Washington, DC: U. S.Government; 1966.

[60] Kong, E. Minimal cerebral palsy: The importance of its recognition. In: Bax M, MacK‐eith R. (ed.) Minimal cerebral dysfunction. Little Club Clinics in DevelopmentalMedicine No. 10. London: Heinemann Medical;1963. p.29-31.

[61] Cermak, S. Developmental dyspraxia. In Roy EA. (ed.), Neuropsychological studiesof apraxia and related disorders. Amsterdam: North-HoUand; 1985. p.225-248.

[62] Polatajko, H. J., Fox, A. M., & Missiuna, C. An international consensus on childrenwith developmental coordination disorder. Canadian Journal of Occupational Thera‐py 1995;62(1) 3-6.

[63] Morris PR, Whiting HTA. Motor impairment and compensatory education. Philadel‐phia: G. Bell and Sons; 1971.

[64] Bernstein NA., Latash ML, Latash MT. Dexterity and Its Development. NY: Psyholo‐gy Press;1996.

[65] Dayan E, Cohen LG. Neuroplasticity subserving motor skill learning. Neuron2011;72(3) 443-454.

[66] Fitts PM. The information capacity of the human motor system in controlling the am‐plitude of movement. Journal of Experimental Psychology 1954;47 381-391.

[67] Cermak S. A., Larkin, D.Developmental coordination disorder. Albany, NY: Delmar/Thompson Learning; 2002


57

[68] Shumway-Cook A. Woollacott MH. Motor Control: Translating Research into Clini‐cal Practice. Philadelphia: Lippincott Williams & Wilkins; 2011.

[69] Fitts PM, Posner MI. Human performance. Belmont, CA: Brooks/Cole;1967.

[70] Hikosaka O, Nakamura K, Sakai K, Nakahara H. Central mechanisms of motor skilllearning. Curr Opin Neurobiol 2002 ;12(2) 217-222.

[71] Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning ofmotor skills. Curr Opin Neurobiol 2005;15(2) 161-167.

[72] Culham JC, Cavina-Pratesi C, Singhal A. The role of parietal cortex in visuomotorcontrol: what have we learned from neuroimaging? Neuropsychologia 2006; 44(13)2668–2684.

[73] Heilman, K. M., & Valenstein, E. Clinical neuropsychology (4th ed.).New York, NY:Oxford University Press; 2003.

[74] Zwicker JG, Missiuna C, Boyd LA. Neural correlates of developmental coordinationdisorder: a review of hypotheses. J Child Neurol 2009;24(10) 1273-1281.

[75] Cummins A, Piek JP, Dyck MJ. Motor coordination, empathy, and social behaviourin school-aged children. Dev Med Child Neurol 2005;47(7) 437-442.

[76] Adolphs R, Damasio H, Tranel D, Damasio AR. Cortical systems for the recognitionof emotion in facial expressions. J Neurosci 1996;16(23) 7678-7687.

[77] Wilson PH, Maruff P, Lum J. Procedural learning in children with developmental co‐ordination disorder. Hum Mov Sci 2003;22(4-5) 515–526.

[78] Wilson PH, Maruff P, Butson M, Williams J, Lum J, Thomas PR. Internal representa‐tion of movement in children with developmental coordination disorder: a mentalrotation task. Dev Med Child Neurol 2004; 46(11) 754–759.

[79] Hyde C, Wilson P. Online motor control in children with developmental coordina‐tion disorder: chronometric analysis of double-step reaching performance. ChildCare Health Dev 2011;37(1) 111-122.

[80] Maruff P,Wilson PH, Trebilcock M, Currie J. Abnormalities of imaged motor sequen‐ces in children with developmental coordination disorder. Neuropsychologia1999;37(11) 1317–1324.

[81] Williams J, Thomas PR, Maruff P, Wilson PH. The link between motor impairmentlevel and motor imagery ability in children with developmental coordination disor‐der. Hum Mov Sci 2008; 27(2) 270–285.

[82] Wilson PH, Thomas PR, Maruff P. Motor imagery training ameliorates motor clumsi‐ness in children. J Child Neurol 2002; 17(7) 491–498.

[83] Geuze RH. Postural control in children with developmental coordination disorder.Neural Plast 2005;12(2-3) 183-196; discussion 263-272.


[84] Kagerer FA, Contreras-Vidal JL, Bo J, Clark JE. Abrupt, but not gradual visuomotordistortion facilitates adaptation in children with developmental coordination disor‐der. Hum Mov Sci 2006;25(4-5) 622-633.

[85] Van Waelvelde H, De Weerdt W, De Cock P, Janssens L, Feys H, Smits EngelsmanBC. Parameterization of movement execution in children with developmental coordi‐nation disorder. Brain Cogn 2006;60(1) 20-31.

[86] Groenewegen HJ. The basal ganglia and motor control. Neural Plast 2003;10(1-2)107-120.

[87] Gheysen F, Van Waelvelde H, Fias W. Impaired visuo-motor sequence learning inDevelopmental Coordination Disorder.Res Dev Disabil 2011;32(2) 749-756.

[88] Gheysen F, Van Opstal F, Roggeman C, Van Waelvelde H, Fias W. Hippocampalcontribution to early and later stages of implicit motor sequence learning. Exp BrainRes 2010;202(4) 795-807.

[89] Sigmundsson H, Ingvaldsen RP, Whiting HT. Inter- and intrasensory modalitymatching in children with hand-eye coordination problems: exploring the develop‐mental lag hypothesis. Dev Med Child Neurol 1997;39(12) 790-796.

[90] Werner JM, Cermak SA., Aziz-Zadeh L. Neural Correlates of Developmental Coordi‐nation Disorder: The Mirror Neuron System Hypothesis. Journal of Behavioral andBrain Science 2012;2,:258-268 doi:10.4236/jbbs.2012.22029 (accessed May 2007)

[91] Scott A. Huettel, Allen W. Song, Gregory McCarthy. Functional Magnetic ResonanceImaging. Massachusetts: Sinauer Associates Inc.; 2009

[92] Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A de‐fault mode of brain function. Proc Natl Acad Sci U S A 2001;98(2) 676-682.

[93] Harrison BJ, Pujol J, López-Solà M, Hernández-Ribas R, Deus J, Ortiz H, Soriano-MasC, Yücel M, Pantelis C, Cardoner N. Consistency and functional specialization in thedefault mode brain network. Proc Natl Acad Sci U S A 2008;105(28) 9781-9886.

[94] Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy,function, and relevance to disease. Ann N Y Acad Sci 2008;1124 1-38.

[95] Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity dis‐tinguishes Alzheimer's disease from healthy aging: evidence from functional MRI.Proc Natl Acad Sci U S A. 2004;101(13) 4637-46742.

[96] Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci BiobehavRev 2009;33(3) 279-296.

[97] Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V. Development offunctional and structural connectivity within the default mode network in youngchildren. Neuroimage 2010;52(1) 290-301.


59

[98] Hillier, S. Intervention for children with developmental coordination disorder: A sys‐tematic review. The Internet Journal of Allied Health Sciences and Practice 2007;5(3)http://ijahsp.nova.edu

[99] Sugden D. Current approaches to intervention in children with developmental coor‐dination disorder. Dev Med Child Neurol 2007;49(6) 467-471.

[100] Pless M, Carlsson M. Effects of motor skill intervention on developmental coordina‐tion disorder: a meta-analysis. Adapt Phys Activ Q 2000;17(4) 381–401.

[101] Wilson PH. Practitioner review: approaches to assessment and treatment of childrenwith DCD: an evaluative review. J Child Psychol Psychiatry 2005; 46(8) 806–823.

[102] Braun SM, Beurskens AJ, Borm PJ, Schack T, Wade DT. The effects of mental practicein stroke rehabilitation: a systematic review. Arch Phys Med Rehabil 2006;87(6)842-852.

[103] Langhorne P, Coupar F, Pollock A.. Motor recovery after stroke: a systematic review.Lancet Neurol. 2009;8(8) 741-754.

[104] Guillot A, Collet C, Nguyen VA, Malouin F, Richards C, Doyon J. Functional neuroa‐natomical networks associated with expertise in motor imagery. Neuroimage2008;41(4) 1471-1483.


brain mapping of developmental coordination disorder...this chapter discusses the brain mapping of...

Documents